Stain-Adaptive Self-Supervised Learning for Histopathology Image Analysis

TL;DR

This paper introduces SASSL, a self-supervised learning method with domain-adversarial training that enhances stain-invariant feature extraction in histopathology images, leading to improved performance across various analysis tasks.

Contribution

The paper presents a novel stain-adaptive self-supervised learning framework that effectively handles stain variations and can be integrated with different downstream histopathology analysis modules.

Findings

Achieved state-of-the-art results on multiple public datasets.

Enhanced robustness of features against stain variations.

Improved downstream task performance with the proposed method.

Abstract

It is commonly recognized that color variations caused by differences in stains is a critical issue for histopathology image analysis. Existing methods adopt color matching, stain separation, stain transfer or the combination of them to alleviate the stain variation problem. In this paper, we propose a novel Stain-Adaptive Self-Supervised Learning(SASSL) method for histopathology image analysis. Our SASSL integrates a domain-adversarial training module into the SSL framework to learn distinctive features that are robust to both various transformations and stain variations. The proposed SASSL is regarded as a general method for domain-invariant feature extraction which can be flexibly combined with arbitrary downstream histopathology image analysis modules (e.g. nuclei/tissue segmentation) by fine-tuning the features for specific downstream tasks. We conducted experiments on publicly available pathological image analysis datasets including the PANDA, BreastPathQ, and CAMELYON16 datasets, achieving the state-of-the-art performance. Experimental results demonstrate that the proposed method can robustly improve the feature extraction ability of the model, and achieve stable performance improvement in downstream tasks.